import re
import datetime
import numpy as np
from itertools import groupby
from skimage import measure
from PIL import Image
from pycocotools import mask

convert = lambda text: int(text) if text.isdigit() else text.lower()
natrual_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)]


def resize_binary_mask(array, new_size):
    image = Image.fromarray(array.astype(np.uint8) * 255)
    image = image.resize(new_size)
    return np.asarray(image).astype(np.bool_)


def close_contour(contour):
    if not np.array_equal(contour[0], contour[-1]):
        contour = np.vstack((contour, contour[0]))
    return contour


def binary_mask_to_rle(binary_mask):
    rle = {'counts': [], 'size': list(binary_mask.shape)}
    counts = rle.get('counts')
    for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
        if i == 0 and value == 1:
            counts.append(0)
        counts.append(len(list(elements)))

    return rle


def binary_mask_to_polygon(binary_mask, tolerance=0):
    """Converts a binary mask to COCO polygon representation
    Args:
        binary_mask: a 2D binary numpy array where '1's represent the object
        tolerance: Maximum distance from original points of polygon to approximated
            polygonal chain. If tolerance is 0, the original coordinate array is returned.
    """
    polygons = []
    # pad mask to close contours of shapes which start and end at an edge
    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
    contours = measure.find_contours(padded_binary_mask, 0.5)
    contours = np.subtract(contours, 1)
    for contour in contours:
        contour = close_contour(contour)
        contour = measure.approximate_polygon(contour, tolerance)
        if len(contour) < 3:
            continue
        contour = np.flip(contour, axis=1)
        segmentation = contour.ravel().tolist()
        # after padding and subtracting 1 we may get -0.5 points in our segmentation 
        segmentation = [0 if i < 0 else i for i in segmentation]
        polygons.append(segmentation)

    return polygons


def create_image_info(image_id, file_name, image_size,
                      date_captured=datetime.datetime.utcnow().isoformat(' '),
                      license_id=1, coco_url="", flickr_url=""):
    image_info = {
        "id": image_id,
        "file_name": file_name,
        "width": image_size[0],
        "height": image_size[1],
        "date_captured": date_captured,
        "license": license_id,
        "coco_url": coco_url,
        "flickr_url": flickr_url
    }

    return image_info


def create_annotation_info(annotation_id, image_id, category_info, binary_mask,
                           image_size=None, tolerance=2, bounding_box=None):
    if image_size is not None:
        binary_mask = resize_binary_mask(binary_mask, image_size)

    binary_mask_encoded = mask.encode(np.asfortranarray(binary_mask.astype(np.uint8)))

    area = mask.area(binary_mask_encoded)
    if area < 1:
        return None

    if bounding_box is None:
        bounding_box = mask.toBbox(binary_mask_encoded)

    if category_info["is_crowd"]:
        is_crowd = 1
        segmentation = binary_mask_to_rle(binary_mask)
    else:
        is_crowd = 0
        segmentation = binary_mask_to_polygon(binary_mask, tolerance)
        if not segmentation:
            return None

    annotation_info = {
        "id": annotation_id,
        "image_id": image_id,
        "category_id": category_info["id"],
        "iscrowd": is_crowd,
        "area": area.tolist(),
        "bbox": bounding_box.tolist(),
        "segmentation": segmentation,
        "width": binary_mask.shape[1],
        "height": binary_mask.shape[0],
    }

    return annotation_info